Biomedical subjects

Woo Kyung Moon

Publications and source records attributed to Woo Kyung Moon.

At least 19 recordsLinked to original sources

PubMed2006

Characterization of benign and malignant solid breast masses: comparison of conventional US and tissue harmonic imaging.

PURPOSE: To prospectively compare the diagnostic performance of radiologists by using conventional ultrasonography (US) and tissue harmonic imaging for the differentiation of benign from malignant solid breast masses, with histologic results used as the reference standard. MATERIALS AND METHODS: The study was approved by the institutional review board, and informed consent was obtained from all patients. Images were obtained with conventional US and tissue harmonic imaging in 88 patients (age range, 25-67 years; mean age, 45 years) with 91 solid breast masses (30 cancers and 61 benign lesions) before excisional or needle biopsy. Three experienced radiologists, who did not perform the examinations, independently analyzed the US findings and provided a level of suspicion to indicate the probability of malignancy. Results were evaluated by using kappa statistics and receiver operating characteristic (ROC) analyses. RESULTS: Regarding the descriptions of US findings, echogenicity (kappa=0.205) was the most discordant between conventional US and tissue harmonic imaging, followed by margin (kappa=0.495), lesion boundary (kappa=0.495), calcifications (kappa=0.537), posterior acoustic transmission (kappa=0.546), echotexture (kappa=0.586), shape (kappa=0.591), and orientation (kappa=0.594). The area under the ROC curve (Az) for conventional US and tissue harmonic imaging was 0.84 and 0.79, respectively, for reader 1; 0.88 and 0.85, respectively, for reader 2; and 0.91 and 0.89, respectively, for reader 3. The overall Az value for the three readers was 0.88 for conventional US and 0.84 for tissue harmonic imaging (95% confidence interval: -0.0950, 0.1646; P=.595). CONCLUSION: The performance of the radiologists with respect to the characterization of solid breast masses as benign or malignant was not significantly improved with tissue harmonic imaging.

Adult↗

PubMed2006

Lymph node metastasis: ultrasmall superparamagnetic iron oxide-enhanced MR imaging versus PET/CT in a rabbit model.

PURPOSE: To prospectively compare the diagnostic accuracy of ultrasmall superparamagnetic iron oxide (USPIO)-enhanced magnetic resonance (MR) imaging and integrated positron emission tomography-computed tomography (PET/CT) for the depiction of lymph node metastasis in an animal model, with histologic findings as the reference standard. MATERIALS AND METHODS: This experiment was approved by the local animal care committee. VX2 carcinoma was implanted into the thighs of 11 rabbits 4 weeks before the imaging study. T2- and T2*-weighted MR examinations were performed 24 hours after USPIO administration, followed by integrated PET/CT. USPIO-enhanced MR imaging and PET/CT analysis for the evaluation of the presence of metastasis in iliac lymph nodes were performed independently by two radiologists and two nuclear medicine physicians, respectively, without histopathologic knowledge. Results were evaluated by using receiver operating characteristic (ROC) analysis, and sensitivities and specificities were compared by using a Z test. RESULTS: Metastases were histopathologically confirmed in 22 of 62 iliac lymph nodes. USPIO-enhanced MR imaging showed a significantly greater area under the ROC curve than did PET/CT (0.984 vs 0.852; P=.023). The respective sensitivity and specificity for the detection of lymph node metastasis were 91% (20 of 22) and 95% (38 of 40) for USPIO-enhanced MR imaging and 64% (14 of 22) and 98% (39 of 40) for PET/CT. In terms of sensitivity, a significant difference was found between USPIO-enhanced MR imaging and PET/CT, particularly for nodal metastasis of less than 5 mm (86% [six of seven] vs 0% [zero of seven]; P=.031), whereas the specificity of the two imaging modalities was similar (P=.226). CONCLUSION: USPIO-enhanced MR imaging results in higher diagnostic accuracy for depicting lymph node metastasis than does PET/CT.

Animals↗

PubMed2006

Cervical lymph node metastases: MR imaging of gadofluorine M and monocrystalline iron oxide nanoparticle-47 in a rabbit model of head and neck cancer.

PURPOSE: To prospectively compare the accuracy of gadofluorine M with that of monocrystalline iron oxide nanoparticle (MION)-47 for the depiction of cervical lymph node metastases at magnetic resonance (MR) imaging in a rabbit model of head and neck cancer by using histologic analysis as the reference standard. MATERIALS AND METHODS: Experiments were approved by the animal care committee. VX2 carcinomas were implanted in both ears of 11 rabbits 4 weeks before MR imaging. T2-weighted, T2*-weighted, and T1-weighted MR images were acquired, and sequential T1-weighted MR imaging was performed immediately and 30 minutes after administration of gadofluorine M (0.05 mmol gadolinium per kilogram body weight). T2-weighted and T2*-weighted MR imaging were performed 24 hours after administration of MION-47 (2.6 mg iron per kilogram body weight). Gadofluorine M- and MION-47-enhanced MR imaging were performed separately and independently by two radiologists who had no knowledge of histopathologic results, and the presence of metastases in lymph nodes was evaluated. A receiver operating characteristic analysis was conducted to compare the diagnostic value of gadofluorine M- and MION-47-enhanced MR imaging. RESULTS: Metastases were confirmed in 20 of 77 lymph nodes at histopathologic analysis. The area under the curve was significantly greater for gadofluorine M-enhanced MR imaging (0.997 and 0.981 for readers 1 and 2, respectively) than for MION-47-enhanced MR imaging (0.889 and 0.846 for readers 1 and 2, respectively). For gadofluorine M-enhanced MR imaging, sensitivity was 100% for both readers and specificity was 89.5% for reader 1 and 87.7% for reader 2. For MION-47-enhanced MR imaging, sensitivity was 80.0% for both readers and specificity was 75.4% for reader 1 and 71.9% for reader 2. CONCLUSION: Gadofluorine M-enhanced MR imaging has higher accuracy for depicting lymph node metastases than does MION-47-enhanced MR imaging.

Animals↗

PubMed2006

Differentiating benign from malignant solid breast masses: comparison of two-dimensional and three-dimensional US.

PURPOSE: To compare prospectively obtained static two-dimensional (2D) and three-dimensional (3D) ultrasonographic (US) images in the diagnostic performance of radiologists with respect to the differentiation of benign from malignant solid breast masses with histopathologic examination as the reference standard. MATERIALS AND METHODS: This study had institutional review board approval, and patient informed consent was obtained. Conventional 2D and 3D US images were obtained from 141 patients (age range, 25-71 years; mean age, 46 years) with 150 solid breast masses (60 cancers and 90 benign lesions) before excisonal or needle biopsy. Four radiologists who had not performed the examinations independently reviewed 2D US images and stored 3D US data and provided a level of suspicion concerning probability of malignancy. The sensitivity, specificity, and negative predictive values of 2D images were compared with those of 3D US images. RESULTS: For all readers, 3D US images were the same as or better than 2D US images in terms of sensitivity (100% vs 100% for reader 1; 100% vs 98% for reader 2; 98% vs 93% for reader 3; 93% vs 92% for reader 4), specificity (58% vs 56% for reader 1; 51% vs 46% for reader 2; 83% vs 72% for reader 3; 86% vs 84% for reader 4), and negative predictive values (100% vs 100% for reader 1; 100% vs 98% for reader 2; 99% vs 94% for reader 3; 95% vs 94% for reader 4). These differences, however, were not statistically significant (P > .05). CONCLUSION: The performance of the radiologists with respect to the characterization of solid breast masses with static 2D US images was similar to that with 3D US data.

Adult↗

PubMed2006

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration.

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.

Adult↗

PubMed2006

Computer algorithm for analysing breast tumor angiogenesis using 3-D power Doppler ultrasound.

Angiogenesis provides blood supply for tumor expansion and also increases the opportunity for tumor cells to enter the blood or lymph circulation. Several proangiogenic factors as well as the contribution of the microenvironment to tumor-induced angiogenesis have been identified. Among these, vascular endothelial growth factor (VEGF) and the angiopoietin (Ang) family play a predominant role involved in the growth for endothelial cells. Tumor vessels are structurally and functionally abnormal because of an imbalance of these angiogenic regulators. In contrast to normal vessels, tumor vasculature is highly disorganized, tortuous and dilated, with uneven diameter and excessive branching. In other words, the morphologic features are likely to carry additional clues that, when used in conjunction with more established parameters, can improve the present diagnostic approaches. In our study, we present a new method that helps to capture the morphologic features from three-dimensional (3-D) power Doppler ultrasound (PDUS) images. After narrowing down the vessels into their skeletons using a 3-D thinning algorithm, we extracted seven features including vessel-to-volume ratio, number of vascular trees, number of bifurcation, mean of radius and three tortuosity measures, from the skeleton and applied a neural network to classify the tumors by using these features. In investigations into 221 solid breast tumors, including 110 benign and 111 malignant cases, the p values using the Student's t-test for all features were less than 0.05, indicating that the proposed features were deemed statistically significant. The A(Z) values for these seven features were 0.84, 0.87, 0.84, 0.75, 0.77, 0.79 and 0.69, respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 80.09% (177 of 221), 80.18% (89 of 111), 80% (88 of 110), 80.18% (89 of 111) and 80% (88 of 110), respectively, with an A(Z) value of 0.89. The preliminary results show that the proposed method is feasible and has a good agreement with the diagnosis of the pathologists.

Adult↗

PubMed2006

Computer-aided detection in digital mammography: comparison of craniocaudal, mediolateral oblique, and mediolateral views.

PURPOSE: To retrospectively compare the sensitivity of a computer-aided detection (CAD) system for depicting breast cancer in three digital mammographic views. MATERIALS AND METHODS: This study was conducted with institutional review board approval; informed consent was waived. A commercially available CAD system was applied to the craniocaudal, mediolateral oblique, and mediolateral digital mammographic views of 83 women (mean age, 48 years; range, 30-66 years) with 83 histologically proved breast cancers. Findings were 59 masses and 41 microcalcifications (17 lesions showed both findings; 42 lesions, mass only; and 24 lesions, microcalcification only). The paired t test was used to analyze sensitivity of the CAD system for the detection of cancer in these three mammographic views and in combinations of the views. RESULTS: The sensitivities of the CAD system were 92% (76 of 83) in the craniocaudal view, 83% (69 of 83) in the mediolateral oblique view, and 86% (71 of 83) in the mediolateral view; the differences were not significant (P = .07-.62). Sensitivity increased to 96% (80 of 83) in the craniocaudal plus mediolateral oblique views and to 99% (82 of 83) in the craniocaudal plus mediolateral oblique plus mediolateral views. For masses, the sensitivity of the CAD system was 76% (45 of 59) in the craniocaudal view and 75% (44 of 59) in the mediolateral oblique view and increased to 93% (55 of 59) when mediolateral oblique and craniocaudal views were combined (P < .001). For microcalcifications, sensitivity was 98% (40 of 41) in the craniocaudal view and 95% (39 of 41) in the mediolateral oblique view, and this increased to 100% (41 of 41) when the mediolateral oblique and craniocaudal views were combined (P = .31). CONCLUSION: The sensitivities of the CAD system were not significantly different among these three digital mammographic views. Sensitivity for depicting masses was significantly increased (P < .001) when the craniocaudal view was added to the mediolateral oblique view.

Adult↗

PubMed2005

Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors.

Ultrasound (US) is a useful diagnostic tool to distinguish benign from malignant masses of the breast. It is a very convenient and safe diagnostic method. However, there is a considerable overlap benignancy and malignancy in ultrasonic images and interpretation is subjective. A high performance breast tumors computer-aided diagnosis (CAD) system can provide an accurate and reliable diagnostic second opinion for physicians to distinguish benign breast lesions from malignant ones. The potential of sonographic texture analysis to improve breast tumor classifications has been demonstrated. However, the texture analysis is system-dependent. The disadvantages of these systems which use texture analysis to classify tumors are they usually perform well only in one specific ultrasound system. While Morphological based US diagnosis of breast tumor will take the advantage of nearly independent to either the setting of US system and different US machines. In this study, the tumors are segmented using the newly developed level set method at first and then six morphologic features are used to distinguish the benign and malignant cases. The support vector machine (SVM) is used to classify the tumors. There are 210 ultrasonic images of pathologically proven benign breast tumors from 120 patients and carcinomas from 90 patients in the ultrasonic image database. The database contains only one image from each patient. The ultrasonic images are captured at the largest diameter of the tumor. The images are collected consecutively from August 1, 1999 to May 31, 2000; the patients' ages ranged from 18 to 64 years. Sonography is performed using an ATL HDI 3000 system with a L10-5 small part transducer. In the experiment, the accuracy of SVM with shape information for classifying malignancies is 90.95% (191/210), the sensitivity is 88.89% (80/90), the specificity is 92.5% (111/120), the positive predictive value is 89.89% (80/89), and the negative predictive value is 91.74% (111/121).

Adolescent↗

PubMed2005

3-D ultrasound texture classification using run difference matrix.

Ultrasonography is one of the most useful diagnostic tools for human soft tissue and it is in routine use in nearly all hospitals and many physicians' offices and clinics. However, the diagnosis mostly depends upon the personal experiences of the physicians. Moreover, the surface features and internal architecture of a tumor are not easy to be demonstrated simultaneously using the conventional two-dimensional (2-D) ultrasound. Recently, three-dimensional (3-D) ultrasound has been developed and allows the physician to view the 3-D anatomy. 3-D breast US can provide transverse, longitudinal planes as well as in addition simultaneously the coronal plane. This additional information has been proved to be helpful for clinical applications. In this paper, a new approach of texture classification of 3-D ultrasound breast diagnosis using run difference matrix with neural networks is developed. The test 3-D US image database includes 54 malignant and 161 benign tumors. In the experiments, the area index A(z) under the ROC curve of the proposal 3-D RDM method can achieve 0.9680. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed 3-D RDM method is 91.9%(148/161), 88.9%(48/54), 93.5%(100/107), 87.3%(48/55), and 94.3%(100/105), respectively.

Area Under Curve↗

PubMed2005

Diffractive ultrasound in the evaluation of solid breast masses: preliminary results.

PURPOSE: We sought to evaluate diffractive ultrasound (US) in the characterization of solid breast masses. MATERIALS AND METHODS: Forty-eight patients with solid breast masses (24 malignant and 24 benign) observed at conventional US underwent targeted diffractive US before needle biopsy or surgery. The process of locating the lesion was performed with knowledge of the mammography and conventional US. On diffractive US, imaging features, including shape, brightness, echotexture, margin, and boundary echo were analyzed and odds ratio (OR) were calculated to show how the risk of malignancy was altered by the presence of a given finding. RESULTS: Of the 48 solid breast masses seen on conventional US, 46 (96%) were seen at diffractive US. Diffractive US features suggestive of malignancy were irregular shape (OR 11.5), sono-opacity (OR 204), spiculated margin (OR 17.0), and absent boundary echo (OR 11.5) or thick rim (OR 5.5), whereas those of benignancy were round shape (OR 0.03), sonotransmission (OR 0.07) or isodensity (OR 0.01), well-defined margin (OR 0.03), and thin capsule (OR 0.03; P < 0.05). CONCLUSION: The finding of a sono-opaque mass on diffractive US was highly sensitive for malignancy. Further improvement of diffractive US equipment is needed to increase image quality and to permit inclusion of the axillary tail and deep portions of the breast.

Adult↗

PubMed2005

Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression.

PURPOSE: To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20-67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression--contour difference, shift distance, area difference--and one feature of shape--solidity-were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis. RESULTS: The mean values of contour difference, shift distance, area difference, and solidity were 3.52% +/- 2.12 (standard deviation), 2.62 +/- 1.31, 1.08% +/- 0.85, and 1.70 +/- 1.85 in malignant tumors and 9.72% +/- 4.54, 5.04 +/- 2.79, 3.17% +/- 2.86, and 0.53 +/- 0.63 in benign tumors, respectively. Differences with P < .001 were statistically significant for all four features. Area under the receiver operating characteristic curve (A(Z)) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The A(Z) value of three features of strain was significantly higher than that of the feature of shape (P < .01). The accuracy, sensitivity, specificity, and positive and negative predictive values of US classifications that were based on values for these four features were 87.0% (87 of 100), 85% (34 of 40), 88% (53 of 60), 83% (34 of 41), and 90% (53 of 59), respectively, with an A(Z) value of 0.91. CONCLUSION: Continuous US images obtained with probe compression and computer-aided analysis can aid in classification of benign and malignant breast tumors.

Adult↗

PubMed2005

Differentiation of benign from malignant solid breast masses: conventional US versus spatial compound imaging.

PURPOSE: To compare prospectively the diagnostic performance of radiologists who used conventional ultrasonography (US) with that of radiologists who used spatial compound imaging for the differentiation of benign from malignant solid breast masses. MATERIALS AND METHODS: The study was approved by the institutional review board, and informed consent was obtained. Before excisional or needle biopsy was performed, conventional US and spatial compound images were obtained in 67 patients (age range, 25-67 years; mean age, 45 years) with 75 solid breast masses (21 cancers and 54 benign lesions). Three experienced radiologists who did not perform the examinations independently analyzed US findings and indicated the probability of malignancy. Results were evaluated with kappa statistics and receiver operating characteristic (ROC) analysis. RESULTS: For US findings, the presence of calcifications was the most discordant feature (kappa = 0.372) between conventional US and spatial compound imaging, followed by echotexture (kappa = 0.439), boundary echo (kappa = 0.496), orientation (kappa = 0.518), echogenicity (kappa = 0.523), shape (kappa = 0.526), margin (kappa = 0.569), and posterior acoustic transmission (kappa = 0.669). The area under the ROC curve for conventional US was 0.79 for reader 1, 0.88 for reader 2, and 0.82 for reader 3, and the area under the ROC curve for spatial compound imaging was 0.85 for reader 1, 0.88 for reader 2, and 0.89 for reader 3. The partial area index for conventional US was 0.29 for reader 1, 0.69 for reader 2, and 0.39 for reader 3, and the partial area index for spatial compound imaging was 0.29 for reader 1, 0.65 for reader 2, and 0.39 for reader 3. The difference between the diagnostic performances of the two techniques was not significant (P > .05). CONCLUSION: The performance of the radiologists with respect to the characterization of solid breast masses was not significantly improved with spatial compound imaging.

Adult↗

PubMed2004

VX2 carcinoma in rabbits after radiofrequency ablation: comparison of MR contrast agents for help in differentiating benign periablational enhancement from residual tumor.

PURPOSE: To prospectively compare the accuracy of a blood pool agent, SH L 643A, with that of gadopentetate dimeglumine in differentiating benign periablational enhancement from residual tumor in VX2 carcinomas in rabbits after radiofrequency (RF) ablation. MATERIALS AND METHODS: Experiment was approved by the animal care committee. Sequential MR images were obtained before and with SH L 643A (17 000 Da, 0.05 mmol/kg) and, after a 24-hour interval, gadopentetate dimeglumine (546 Da, 0.1 mmol/kg) in 12 rabbits with VX2 carcinoma in the back muscle prior to (n = 12) and early (n = 12), 1 week (n = 8), and 4 weeks (n = 4) after RF ablation. RF ablation was performed with output of 90 W but at less than 300 seconds to ensure incomplete tumor ablation. The pathologic specimens were sectioned in the same plane as MR imaging, and the enhancement ratios (ie, the ratios of postcontrast to precontrast signal intensity) and the microvessel densities of residual tumor and benign periablational enhancement were assessed. RESULTS: With SH L 643A, the peak enhancement ratios of residual tumor (1.64 +/- 0.31 [standard deviation]) were significantly higher than those of benign periablational enhancement (0.97 +/- 0.16) (P < .001). With gadopentetate dimeglumine, the peak enhancement ratios of residual tumor (1.82 +/- 0.33) were not different from those of benign periablational enhancement (1.71 +/- 0.36). In benign periablational enhancement, enhancement ratios with injection of SH L 643A were lower than those with injection of gadopentetate dimeglumine for all time points up to 30 minutes (P < .05). The microvessel density was 25.72 +/- 5.43 vessels per field of view for residual tumor and 10.37 +/- 2.88 vessels per field of view for benign periablational enhancement (P < .001). CONCLUSION: Blood pool contrast agent SH L 643A permits more accurate differentiation of benign periablational enhancement from residual tumor compared with the extracellular agent gadopentetate dimeglumine.

Animals↗

PubMed2004

Characterization of spiculation on ultrasound lesions.

Spiculation is a stellate distortion caused by the intrusion of breast cancer into surrounding tissue. Its existence is an important clue to characterizing malignant tumors. Many successful mammographic methods have been proposed to detect tumors with spiculation. Traditional two-dimensional (2-D) ultrasound cannot easily find spiculations because spiculations normally appear parallel to the surface of the skin. Recently, three-dimensional (3-D) ultrasound has been gradually used in clinical applications and it has been proven to be useful in determining the architectural distortion or spiculation that surrounds a breast tumor. This paper aims to identify spiculation from 3-D ultrasonic volume data of a tumor found by a physician. In the proposed method, each coronal slice of volume data is successively extracted and then analyzed as a 2-D ultrasound image by the proposed spiculation detection method. First, in each horizontal slice, the modified rotating structuring element (ROSE) operation is used to find the central region in which spiculation lines converge. Second, the stick algorithm is used to estimate the direction of the edge of each pixel around the central region. A pixel whose edge points toward the central region is marked as a potential spiculation. Finally, the marked pixels are collected around the central region and their distribution is analyzed to determine whether spiculation is present. The 3-D test datasets were obtained using the Voluson 530 or 730, Kretztechnik, Austria. First, the proposed method was tested on 104 2-D typical coronal images (selected by an experienced physician) extracted from 52 3-D ultrasonic datasets. Finally, 225 3-D pathologically proven datasets were tested to evaluate the performance. Spiculations are more easily observed in the coronal view than in the other two views. That is, the 3-D ultrasound is a powerful tool for identifying spiculations. Furthermore, 16% (19/120) of benign cases and 90% (94/105) of malignant cases are detected as spiculations.

Algorithms↗

PubMed2004

Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features.

A computer-aided diagnosis (CAD) algorithm identifying breast nodule malignancy using multiple ultrasonography (US) features and artificial neural network (ANN) classifier was developed from a database of 584 histologically confirmed cases containing 300 benign and 284 malignant breast nodules. The features determining whether a breast nodule is benign or malignant were extracted from US images through digital image processing with a relatively simple segmentation algorithm applied to the manually preselected region of interest. An ANN then distinguished malignant nodules in US images based on five morphological features representing the shape, edge characteristics, and darkness of a nodule. The structure of ANN was selected using k-fold cross-validation method with k = 10. The ANN trained with randomly selected half of breast nodule images showed the normalized area under the receiver operating characteristic curve of 0.95. With the trained ANN, 53.3% of biopsies on benign nodules can be avoided with 99.3% sensitivity. Performance of the developed classifier was reexamined with new US mass images in the generalized patient population of total 266 (167 benign and 99 malignant) cases. The developed CAD algorithm has the potential to increase the specificity of US for characterization of breast lesions.

Adolescent↗

PubMed2003

Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis.

Recent statistics show that breast cancer is a major cause of death among women in developed countries. Hence, finding an accurate and effective diagnostic method is very important. In this paper, we propose a high precision computer-aided diagnosis (CAD) system for sonography. We utilize a support vector machine (SVM) to classify breast tumors according to their texture information surrounding speckle pixels. We test our system with 250 pathologically-proven breast tumors including 140 benign and 110 malignant ones. Also we compare the diagnostic performances of three texture features, i.e., speckle-emphasis texture feature, nonspeckle-emphasis texture feature and conventional all pixels texture feature, applied to breast sonography using SVM. In our experiment, the accuracy of SVM with speckle information for classifying malignancies is 93.2% (233/250), the sensitivity is 95.45% (105/110), the specificity is 91.43% (128/140), the positive predictive value is 89.74% (105/117) and the negative predictive value is 96.24% (128/133). Based on the experimental results, speckle phenomenon is a useful tool to be used in computer-aided diagnosis; its performance is better than those of the other two features. Speckle phenomenon, which is considered as noise in sonography, can intrude into judgments of a physician using naked eyes but it is another story for application in a computer-aided diagnosis algorithm.

Algorithms↗

PubMed2003

Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis.

Because ultrasound (US) imaging offers benefits compared with other medical imaging techniques, it is used routinely in nearly all hospitals and many clinics. However, the surface features and internal structure of a tumor are not easily demonstrated simultaneously using the traditional 2-D US. The newly developed three-dimensional (3-D) US can capture the morphology of a breast tumor and overcome the limitations of the traditional 2-D US. This study deals with pixel relation analysis techniques for use with 3-D breast US images and compares its performance to 2-D versions of the images. The 3-D US imaging was performed using a Voluson 530 scanner. The rectangular subimages of the volume-of-interest (VOI) were manually selected and the selected VOIs were outlined to include the entire extent of the tumor margin. The databases in this study included 54 malignant and 161 benign tumors. All solid nodules at US belong over C3 (probably benign) according to ACR BI-RADS category. All or some selected 2-D slices were used separately to calculate the diagnosis features for a 3-D US data set. We have proposed and compared several different methods to extract the characteristics of these consecutive 2-D images. As shown in our experiments, the diagnostic results were better than those of the conventional 2-D US. In the experiments, the area index Az under ROC curve of the proposed 3-D US method can achieve 0.9700 +/- 0.0118, but Az of the 2-D US is only 0.8461 +/- 0.0315. The p value of these two Az differences using z test is smaller than 0.01. Furthermore, we can find that the features from only several slices are enough to provide good diagnostic results if the adopted features are modified from the 2-D features.

Breast Neoplasms↗

PubMed2003

3-D breast ultrasound segmentation using active contour model.

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.

Breast Neoplasms↗